# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for slim.inception_v4."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

from models.google.nets import inception


class InceptionTest(tf.test.TestCase):
    def testBuildLogits(self):
        batch_size = 5
        height, width = 299, 299
        num_classes = 1000
        inputs = tf.random_uniform((batch_size, height, width, 3))
        logits, end_points = inception.inception_v4(inputs, num_classes)
        auxlogits = end_points['AuxLogits']
        predictions = end_points['Predictions']
        self.assertTrue(auxlogits.op.name.startswith('InceptionV4/AuxLogits'))
        self.assertListEqual(auxlogits.get_shape().as_list(),
                             [batch_size, num_classes])
        self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, num_classes])
        self.assertTrue(predictions.op.name.startswith(
            'InceptionV4/Logits/Predictions'))
        self.assertListEqual(predictions.get_shape().as_list(),
                             [batch_size, num_classes])

    def testBuildWithoutAuxLogits(self):
        batch_size = 5
        height, width = 299, 299
        num_classes = 1000
        inputs = tf.random_uniform((batch_size, height, width, 3))
        logits, endpoints = inception.inception_v4(inputs, num_classes,
                                                   create_aux_logits=False)
        self.assertFalse('AuxLogits' in endpoints)
        self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, num_classes])

    def testAllEndPointsShapes(self):
        batch_size = 5
        height, width = 299, 299
        num_classes = 1000
        inputs = tf.random_uniform((batch_size, height, width, 3))
        _, end_points = inception.inception_v4(inputs, num_classes)
        endpoints_shapes = {'Conv2d_1a_3x3': [batch_size, 149, 149, 32],
                            'Conv2d_2a_3x3': [batch_size, 147, 147, 32],
                            'Conv2d_2b_3x3': [batch_size, 147, 147, 64],
                            'Mixed_3a': [batch_size, 73, 73, 160],
                            'Mixed_4a': [batch_size, 71, 71, 192],
                            'Mixed_5a': [batch_size, 35, 35, 384],
                            # 4 x Inception-A blocks
                            'Mixed_5b': [batch_size, 35, 35, 384],
                            'Mixed_5c': [batch_size, 35, 35, 384],
                            'Mixed_5d': [batch_size, 35, 35, 384],
                            'Mixed_5e': [batch_size, 35, 35, 384],
                            # Reduction-A block
                            'Mixed_6a': [batch_size, 17, 17, 1024],
                            # 7 x Inception-B blocks
                            'Mixed_6b': [batch_size, 17, 17, 1024],
                            'Mixed_6c': [batch_size, 17, 17, 1024],
                            'Mixed_6d': [batch_size, 17, 17, 1024],
                            'Mixed_6e': [batch_size, 17, 17, 1024],
                            'Mixed_6f': [batch_size, 17, 17, 1024],
                            'Mixed_6g': [batch_size, 17, 17, 1024],
                            'Mixed_6h': [batch_size, 17, 17, 1024],
                            # Reduction-A block
                            'Mixed_7a': [batch_size, 8, 8, 1536],
                            # 3 x Inception-C blocks
                            'Mixed_7b': [batch_size, 8, 8, 1536],
                            'Mixed_7c': [batch_size, 8, 8, 1536],
                            'Mixed_7d': [batch_size, 8, 8, 1536],
                            # Logits and predictions
                            'AuxLogits': [batch_size, num_classes],
                            'PreLogitsFlatten': [batch_size, 1536],
                            'Logits': [batch_size, num_classes],
                            'Predictions': [batch_size, num_classes]}
        self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
        for endpoint_name in endpoints_shapes:
            expected_shape = endpoints_shapes[endpoint_name]
            self.assertTrue(endpoint_name in end_points)
            self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
                                 expected_shape)

    def testBuildBaseNetwork(self):
        batch_size = 5
        height, width = 299, 299
        inputs = tf.random_uniform((batch_size, height, width, 3))
        net, end_points = inception.inception_v4_base(inputs)
        self.assertTrue(net.op.name.startswith(
            'InceptionV4/Mixed_7d'))
        self.assertListEqual(net.get_shape().as_list(), [batch_size, 8, 8, 1536])
        expected_endpoints = [
            'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'Mixed_3a',
            'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
            'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
            'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a',
            'Mixed_7b', 'Mixed_7c', 'Mixed_7d']
        self.assertItemsEqual(end_points.keys(), expected_endpoints)
        for name, op in end_points.iteritems():
            self.assertTrue(op.name.startswith('InceptionV4/' + name))

    def testBuildOnlyUpToFinalEndpoint(self):
        batch_size = 5
        height, width = 299, 299
        all_endpoints = [
            'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'Mixed_3a',
            'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
            'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
            'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a',
            'Mixed_7b', 'Mixed_7c', 'Mixed_7d']
        for index, endpoint in enumerate(all_endpoints):
            with tf.Graph().as_default():
                inputs = tf.random_uniform((batch_size, height, width, 3))
                out_tensor, end_points = inception.inception_v4_base(
                    inputs, final_endpoint=endpoint)
                self.assertTrue(out_tensor.op.name.startswith(
                    'InceptionV4/' + endpoint))
                self.assertItemsEqual(all_endpoints[:index + 1], end_points)

    def testVariablesSetDevice(self):
        batch_size = 5
        height, width = 299, 299
        num_classes = 1000
        inputs = tf.random_uniform((batch_size, height, width, 3))
        # Force all Variables to reside on the device.
        with tf.variable_scope('on_cpu'), tf.device('/cpu:0'):
            inception.inception_v4(inputs, num_classes)
        with tf.variable_scope('on_gpu'), tf.device('/gpu:0'):
            inception.inception_v4(inputs, num_classes)
        for v in tf.get_collection(tf.GraphKeys.VARIABLES, scope='on_cpu'):
            self.assertDeviceEqual(v.device, '/cpu:0')
        for v in tf.get_collection(tf.GraphKeys.VARIABLES, scope='on_gpu'):
            self.assertDeviceEqual(v.device, '/gpu:0')

    def testHalfSizeImages(self):
        batch_size = 5
        height, width = 150, 150
        num_classes = 1000
        inputs = tf.random_uniform((batch_size, height, width, 3))
        logits, end_points = inception.inception_v4(inputs, num_classes)
        self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, num_classes])
        pre_pool = end_points['Mixed_7d']
        self.assertListEqual(pre_pool.get_shape().as_list(),
                             [batch_size, 3, 3, 1536])

    def testUnknownBatchSize(self):
        batch_size = 1
        height, width = 299, 299
        num_classes = 1000
        with self.test_session() as sess:
            inputs = tf.placeholder(tf.float32, (None, height, width, 3))
            logits, _ = inception.inception_v4(inputs, num_classes)
            self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
            self.assertListEqual(logits.get_shape().as_list(),
                                 [None, num_classes])
            images = tf.random_uniform((batch_size, height, width, 3))
            sess.run(tf.initialize_all_variables())
            output = sess.run(logits, {inputs: images.eval()})
            self.assertEquals(output.shape, (batch_size, num_classes))

    def testEvaluation(self):
        batch_size = 2
        height, width = 299, 299
        num_classes = 1000
        with self.test_session() as sess:
            eval_inputs = tf.random_uniform((batch_size, height, width, 3))
            logits, _ = inception.inception_v4(eval_inputs,
                                               num_classes,
                                               is_training=False)
            predictions = tf.argmax(logits, 1)
            sess.run(tf.initialize_all_variables())
            output = sess.run(predictions)
            self.assertEquals(output.shape, (batch_size,))

    def testTrainEvalWithReuse(self):
        train_batch_size = 5
        eval_batch_size = 2
        height, width = 150, 150
        num_classes = 1000
        with self.test_session() as sess:
            train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
            inception.inception_v4(train_inputs, num_classes)
            eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
            logits, _ = inception.inception_v4(eval_inputs,
                                               num_classes,
                                               is_training=False,
                                               reuse=True)
            predictions = tf.argmax(logits, 1)
            sess.run(tf.initialize_all_variables())
            output = sess.run(predictions)
            self.assertEquals(output.shape, (eval_batch_size,))


if __name__ == '__main__':
    tf.test.main()
